knitr::opts_chunk$set(echo = TRUE, warning= FALSE, message = FALSE)

library(tidyverse)
library(here)
library(sf)
library(tmap)

# can update packages with update.packages(ask = FALSE)

Read in the data

sf_trees <- read_csv(here('data', 'sf_trees', 'sf_trees.csv'),
                    show_col_types = FALSE)

Part 1: wrangling and ggplot review

** Example 1:** Find counts of observation by ‘legal_status’ & wrangle a bit.

### Method 1: group_by() %>% summarize()
sf_trees %>% 
  group_by(legal_status) %>% 
  summarize(tree_count = n()) 
## # A tibble: 10 × 2
##    legal_status                 tree_count
##    <chr>                             <int>
##  1 DPW Maintained                   141725
##  2 Landmark tree                        42
##  3 Permitted Site                    39732
##  4 Planning Code 138.1 required        971
##  5 Private                             163
##  6 Property Tree                       316
##  7 Section 143                         230
##  8 Significant Tree                   1648
##  9 Undocumented                       8106
## 10 <NA>                                 54
### Method 2: different way plus a few new functions
top_5_status <- sf_trees %>% 
  count(legal_status) %>% 
  drop_na(legal_status) %>% # will drop any row with an na in it if just (), but if you specify it will drop na in that col
  rename(tree_count = n) %>% 
  relocate(tree_count) %>% 
  slice_max(tree_count, n = 5) %>% 
  arrange(desc(tree_count)) # - sign in front of tree_count puts in descending order OR `(desc())`

Make a graph of the top 5 from above:

ggplot(data = top_5_status, aes(x = fct_reorder(legal_status, tree_count), y = tree_count)) +
  geom_col(fill = 'darkgreen') +
  labs(x = 'Legal status', y = 'Tree count') +
  coord_flip() +
  theme_minimal()

** Example 2:** Only going to keep observations where legal status is “Permitted Site” and caretaker is “MTA”, and store as permitted_data_df

shift-cmd-c to comment/uncomment quickly

# sf_trees$legal_status %>% unique()
# unique(sf_trees$caretaker)
permitted_data_df <- sf_trees %>% 
  filter(legal_status %in% c('Permitted Site', 'Private') & caretaker == 'MTA')
# & and , work the same, but | means 'or'

** Example 3:** Only keep Blackwood Acacia trees, and then only keep columns ‘legal_status’, ‘date’, ‘latitude’, ‘longitude’, and store as ‘blackwood_acacia_df’

blackwood_acacia_df <- sf_trees %>%
  filter(str_detect(species, 'Blackwood Acacia')) %>% 
  select(legal_status, date, lat = latitude, lon = longitude)

### Make a little graph of locations
ggplot(data = blackwood_acacia_df, aes(x = lon, y = lat)) +
  geom_point(color = 'darkgreen')

** Example 4:** use tidyr::separate()

sf_trees_sep <- sf_trees %>% 
  separate(species, into = c('spp_scientific', 'spp_common'), sep = ' :: ')

** Example 5:** use tidyr::unite()

ex_5 <- sf_trees %>% 
  unite('id_status', tree_id, legal_status, sep = '_COOL_')

Part 2: make some maps

Step 1: make some maps

Step 1: convert the lat/lon to spatial point, st_as_sf()

blackwood_acacia_sf <- blackwood_acacia_df %>% 
  drop_na(lat, lon) %>% 
  st_as_sf(coords = c('lon', 'lat'))

### we need to tell R what the coordinate reference system is 
st_crs(blackwood_acacia_sf) <- 4326

ggplot(data = blackwood_acacia_sf) + 
  geom_sf(color = 'darkgreen') +
  theme_minimal()

Read in the SF shapefile and add to map

sf_map <- read_sf(here('data', 'sf_map', 'tl_2017_06075_roads.shp'))

sf_map_transform <- st_transform(sf_map, 4326)

ggplot(data = sf_map_transform) +
  geom_sf()

Combine the maps!

ggplot() +
  geom_sf(data = sf_map,
          size = .1, 
          color = 'darkgrey') +
  geom_sf(data = blackwood_acacia_sf, 
          color = 'red', 
          size = 0.5) +
  theme_void() + 
  labs(title = 'Blackwood acacias in SF')

Now an interactive map!

tmap_mode('view')

tm_shape(blackwood_acacia_sf) + 
  tm_dots()